Sequence Specificity Analysis

Introduction of Sequence Specificity Analysis

DNA binding and RNA binding proteins play an important role in both the field of gene analysis and gene regulation, including transcription and alternative splicing, and the specific pattern sequences of DNA and RNA binding proteins also have a guiding role in the discovery of pathogenic genes. However, with the development of molecular biology, the amount of biological data has exploded. The traditional method of finding sequence specificity through biological experiments and statistical analysis has been difficult to dig out useful data from massive data, and it will take a lot of time and labor costs.

As artificial intelligence matures and develops, it is widely used in life science research, for example, deep learning method techniques were widely used in sequence specificity analysis. Convolutional Neural Network (CNN) is a new deep learning technology in recent years. It is widely used in the fields of image, speech recognition and natural language processing, and its development is relatively mature. In the process of sequence specificity analysis, the focus is on the phantom sequence prediction motif in the phantom recognition problem, and the sequence prediction motif established by the convolutional neural network can greatly improve the phantom prediction ability.

Fig 1.Analysis process of sequence specificity analysis.

With the development of splicing mutation research, various prediction software and algorithm models are used for mutation analysis. Because different software is based on different algorithms, there are certain limitations. In addition to providing artificial intelligence-related analysis methods, Protheragen will also provide comprehensive multi-software joint analysis based on customer data to maximize the accuracy of prediction results. Our data analysis service process is as follows:

• Raw data: the sequence specificities of DNA- and RNA-binding proteins sequencing data can be measured by several types of high-throughput assay, including PBM, SELEX, and ChIP- and CLIP-seq techniques.
• Capturing these binding specificities from raw sequence data by jointly discovering new sequence motifs along with rules for combining them into a predictive binding score.
• Identify binding sites in test sequences and score the effects of novel mutations.
• Generate a complete analysis result report.

Sequence specific DNA and RNA binding proteins are vital in normal cellular control and in disease states such as cancer and specific genetic diseases. Sequence specificity analysis can be applied to the following fields:

• DNA protein binding analysis.
• RNA binding protein analysis.
• Gene regulation analysis.
• Promoters analysis.
• Transcription factor diversity analysis.
• Cancer and genetic disease research.
• Drug Target Research.

CD ComputaBio provides sequence specificity analysis based on artificial intelligence methods. The application of artificial intelligence methods such as convolutional neural network method has improved accuracy of sequence-specific prediction motif. Here, we provide different algorithm model and software (such as DeepBind, DeepSEA, etc.) for promoters, gene regulatory proteins or transcription factors sequence specificity analysis according to customer data. Protheragen provides one-stop data analysis services, you only need to upload raw sequencing data (captured from PBM, SELEX, and ChIP-seq, CLIP-seq or other techniques), we will use artificial intelligence to analyze data and generate a complete analysis result report for you. For specificity analysis, if you have any questions, please feel free to contact us for details, we have a professional technical support team to answer your questions.

• DeepBind Based Analysis

References

• Alipanahi, B, et al.Predicting the sequence specificities of DNA - and RNA - binding proteins by deep learning [J]. Nature Biotechnology. 2015. 33: 831–838.
• Matthew T. W, et al. Evaluation of methods for modeling transcription-factor sequence specificity. [J]. Nat Biotechnol. 2013 Feb; 31(2): 126–134.
• Lambert, S.A, et al. Similarity regression predicts evolution of transcription factor sequence specificity [J]. Nat Genet . 2019. 51, 981–989.